The background description provided herein is for the purpose of presenting the context of the present disclosure. Work of the inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure. Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in the present disclosure, and are not admitted to be prior art by inclusion in this section.
Online advertising is a multibillion dollar business, and is growing as use of the internet becomes more pervasive. With this in mind, there are four primary players in the online advertising ecosystem, namely an advertiser, an ad-network, a publisher, and an end user. Generally, an advertiser is a party that has an advertisement (or “ad”) that it wishes to distribute to end users. The advertiser is typically willing to pay a third party, e.g., the ad-publisher, to publish its ads. An ad-network may act as an intermediary between advertisers and publishers. For example, an ad-network may collect a plurality of ads from advertisers and place them in publication space, e.g., via ad space on web-sites, computer programs, or other utilities that the end user may use.
The general relationship of these parties is illustrated in FIG. 1. As shown, advertising ecosystem 100 includes advertiser 100, which provides ads to ad network 102. Ad network 102 transmits certain ads to publisher 103, which publishes them in ad space (not shown) which may be viewed by user 104. Publisher 103 may be paid by one or both of ad network 102 and advertiser 101 or both for its publication services. Ad network 102 may track ads published by publisher 103 and provide billing services for advertiser 101.
To create value for advertisers, ad-networks may also perform user tracking, which may aim to associate user behavior with respect to published ads. The goal of user tracking may be, for example, to associate user behavior with the user's identity and/or characteristics of the user, e.g., his or her interests, preferences, spending habits, etc. By way of example, ad network may collect ad delivery statistics from publisher 103 to determine which ads were published, which ads were interacted with by user 104 (e.g., clicked), which ads were ignored by user 104, etc. By manipulating these statistics, ad network 102 may be able to determine user 104's interests and/or identity, something user 104 may wish to protect. This issue is of particular in low-distribution ad-campaigns, where an individual or a small group is targeted with an ad that is specific to a particular interest or topic.
Online advertising ecosystems such as the one shown in FIG. 1 can therefore raise privacy concerns for users. Although technologies have been developed to address some of these concerns, they may require users to trust a third party (e.g., a trusted third party) with their privacy sensitive information. If the trusted third party is compromised or is in fact not trustworthy, unwanted distribution of user privacy sensitive information may result.
Although the following Detailed Description will proceed with reference being made to illustrative embodiments, many alternatives, modifications, and variations thereof will be apparent to those skilled in the art.